Welcome to Anbu Huang’s HomePage

About me:

I study diffusion/flow-matching models and multimodal foundation models, for which you can find more in my blog post.

• On the Generative side: I study the “impossible triangle” of high fidelity, fast sampling, and strong controllability—designing training and inference methods (distillation, guidance, solver-aware sampling) that expand the Pareto frontier.

• On the Multimodal side: I study to build native multimodal architectures that achieve a true unification of understanding and generation, enabling AI to perceive, reason, and create within a single, cohesive framework.

Contact Me: huanganbu@gmail.com

Previously (2014 - 2023): I focused on applied AI, including recommendation systems, federated learning, and AI safety, building scalable solutions that prioritized privacy, security, and user impact.

• Recommendation Systems: At Tencent, I led a technical team to develop the world’s largest online music recommender system, serving over 100 million users globally. We utilized AI techniques like natural language processing, music information retrieval, and deep neural networks to analyze listening histories and predict user preferences, modeling human emotions and interests.
• Federated Learning and Privacy Computing: I engaged in theoretical and practical aspects of trustworthy machine learning, participating in the design of the FATE framework, developing a federated vision system, and publishing several important research papers on federated learning .
• AI Safety and Anti-Spoofing: To address risks from technologies like Deepfakes, I contributed to facial verification systems, including liveness detection and recognition, while developing frameworks to detect biases, adversarial attacks, and ensure ethical AI deployment.